Browsing by Subject "social media"

Now showing 1 - 10 of 65
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    #accessibilityFail: Categorizing Shared Photographs of Physical Accessibility Problems
    (ACM, 2016-10) Li, Hanlin; Brady, Erin; Department of Human-Centered Computing, School of Informatics and Computing
    Social media platforms are existing online spaces where users share their daily encounters, providing a large dataset of photographs of inaccessible environments. We analyzed 100 posts from Twitter and Instagram that describe accessibility problems. Our findings suggest these posts are helpful to locate, identify and communicate accessibility problems, and provide design ideas for potential assistive technologies. We suggest design implications using social media posts to improve physical accessibility.
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    Advocacy in Mental Health Social Interactions on Public Social Media
    (2022-02) Cornet, Victor P.; Holden, Richard J.; Bolchini, Davide; Brady, Erin; Mohler, George; Hong, Michin; Lee, Sangwon
    Health advocacy is a social phenomenon in which individuals and collectives attempt to raise awareness and change opinions and policies about health-related causes. Mental health advocacy is health advocacy to advance treatment, rights, and recognition of people living with a mental health condition. The Internet is reshaping how mental health advocacy is performed on a global scale, by facilitating and broadening the reach of advocacy activities, but also giving more room for opposing mental health advocacy. Another factor contributing to mental health advocacy lies in the cultural underpinnings of mental health in different societies; East Asian countries like South Korea have higher stigma attached to mental health compared to Western countries like the US. This study examines interactions about schizophrenia, a specific mental health diagnosis, on public social media (Facebook, Instagram, and Twitter) in two different languages, English and Korean, to determine how mental health advocacy and its opposition are expressed on social media. After delineation of a set of keywords for retrieval of content about schizophrenia, three months’ worth of social media posts were collected; a subset of these posts was then analyzed qualitatively using constant comparing with a proposed model describing online mental heath advocacy based on existing literature. Various expressions of light mental health advocacy, such as sharing facts about schizophrenia, and strong advocacy, showcasing offline engagement, were found in English posts; many of these expressions were however absent from the analyzed Korean posts that heavily featured jokes, insults, and criticisms. These findings were used to train machine learning classifiers to detect advocacy and counter-advocacy. The classifiers confirmed the predominance of counter-advocacy in Korean posts compared to important advocacy prevalence in English posts. These findings informed culturally sensitive recommendations for social media uses by mental health advocates and implications for international social media studies in human-computer interaction.
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    Are Recent Terrorism Trends Reflected in Social Media?
    (IEEE, 2017-10) Terziyska, Ivana; Shah, Setu; Luo, Xiao; Engineering Technology, School of Engineering and Technology
    Social media plays an important role in shaping the beliefs and sentiments of an audience regarding an event. A comparison between public data sets that have holistic features and social media data set that include more user features would give insight into the spread of misinformation and aspects of events that are reflected in user behavior. In this research, we compare the trends identified in the public data set - Global Terrorism Database (GTD) with the trends reflected through the social media data obtained using the Twitter API. The unsupervised learning algorithm Self-Organizing Map (SOM) is used to identify the features and trends summarized by the clusters. The results show discrepancies in the features and related trends of terrorism events in the GTD data set and obtained Twitter data set to suggest some media bias and public perception on terrorism.
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    Attitudes About 'Fair Use' and Content Sharing in Social Media Applications
    (ACM, 2017-02) Faklaris, Cori; Hook, Sara Anne; Human-Centered Computing, School of Informatics and Computing
    The shift to Social Networking Services (SNSs) and mobile messaging apps such as Facebook, Instagram and Snapchat that rely on User-Generated Content (UGC) has challenged notions of fair use under U.S. copyright law. It remains unclear what understandings are common among these app users regarding legal and ethical norms in reusing artistic, journalistic and other types of content outside of online remixer spaces. Our online survey of N=106 users of N=48 SNS platforms and apps measured attitudes regarding fair use under U.S. copyright law and attribution for work that is shared. Participants reported a high level of agreement with more-restrictive conditions for content publishing and reuse. However, analyses of ratings and responses to open-ended questions reveal tension between issues of intellectual integrity and intellectual property.
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    BlogSum: A Query-based Summarization Approach to Make Sense of Social Media
    (Office of the Vice Chancellor for Research, IUPUI, 2016-04-08) Mithun, Shamima
    With the rapid growth of the Social Web, a large amount of informal opinionated texts are available on numerous topics. However, people can be overwhelmed with this vast amount of information and they need help to find the information of their interests. Natural language tools for automatically analyzing these opinions become necessary to help individuals, organizations, and governments in making timely decisions. To address this need, I proposed a summarization approach for opinionated texts. To validate my approach, BlogSum is developed and evaluated experimentally using current benchmarks. Users can ask BlogSum any question (e.g. Why do people like Chrome better than Firefox?). To answer user's question, BlogSum first retrieves relevant blogs, reviews from the web then generates a concise summary that represents people opinions expressed towards the topic. Since blog summarization is a more recent endeavor, an error analysis was conducted by manually analyzing blog summaries to find there is any information processing difference needed for blogs compared to factual data. This analysis shows that question irrelevance and discourse incoherence, which decrease the overall quality of a summary and reduces the summary coherence, are two major issues for blog summaries. To address question irrelevance and discourse incoherence, in this work a domain-independent schema-based summarization approach is developed that utilizes discourse structures. This approach is based on the automatic identification of discourse relations within candidate sentences in order to instantiate the most appropriate discourse schema and filter and order candidate sentences in the most effective way. BlogSum also needs to deal with opinions, emotions effectively to be successful. BlogSum's overall performance as well as performance for question relevance and coherence was evaluated using various dataset. These results show that the proposed approach can effectively reduce question irrelevance and discourse incoherence and satisfy user's information need.
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    Charitable Crowdfunding: Who Gives, to What, and Why?
    (2021-03-31) Osili, Una; Bergdoll, Jon; Pactor, Andrea; Ackerman, Jacqueline; Houston, Peter
    The growth of online giving signals a promising future for crowdfunding and offers donors another avenue for their generosity. This report provides details about how crowdfunding fits within the philanthropic landscape, who crowdfunding donors are, their motivations for using this giving vehicle, how they differ from typical charitable donors, the kinds of causes they support, and both donor and non-donor perceptions of this giving vehicle. Additionally, results from survey questions about charitable behavior during the COVID-19 pandemic and national reckoning on social and racial justice enhance the report.
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    Comparing American soccer dialogues: social media commentary Surrounding the 2014 US men’s and 2015 US women’s World Cup teams
    (Taylor & Francis, 2018) Burch, Lauren M.; Billings, Andrew C.; Zimmerman, Matthew H.; IUPUC Division of Business
    Mega sporting events such as the World Cup have been found to stimulate categorization of in-groups and out-groups among fans. While self-categorization correlates with gender, the sport of soccer also facilitates nationalistic categorization. The World Cup features nation vs. nation competition while making gender a non-variable as the men and women compete in separate tournaments in separate years. This study examined 33,529 tweets illustrating social media match commentary involving US teams and opponents on Twitter during the 2014 and 2015 World Cups. Results revealed US teams were more likely to be described in regard to attributions of success and failure, while opposition teams were more likely to receive personal and physical attributions. Conversely, no differences were found between US Men’s and Women’s teams in regard to characterizations of success and failure, but revealed the Women’s team was more likely to receive personal and physical characterizations.
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    Daily Situational Brief, May 2, 2011
    (MESH Coalition, 5/2/2011) MESH Coalition
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    A Decade of Facebook: Can We Still Be Friends?
    (2013-12) Lamb, Annette; Johnson, Larry
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    Does Bad News Spread Faster?
    (IEEE, 2017-01) Fang, Anna; Ben-Miled, Zina; Electrical and Computer Engineering, School of Engineering and Technology
    Bad news travels fast. Although this concept may be intuitively accepted, there has been little evidence to confirm that the propagation of bad news differs from that of good news. In this paper, we examine the effect of user perspective on his or her sharing of a controversial news story. Social media not only offers insight into human behavior but has also developed as a source of news. In this paper, we define the spreading of news by tracking selected tweets in Twitter as they are shared over time to create models of user sharing behavior. Many news events can be viewed as positive or negative. In this paper, we compare and contrast tweets about these news events among general users, while monitoring the tweet frequency for each event over time to ensure that news events are comparable with respect to user interest. In addition, we track the tweets of a controversial event between two different groups of users (i.e., those who view the event as positive and those who view it as negative). As a result, we are able to make assessments based on a single event from two different perspectives.